Authors:
- With the development of modeling techniques, it has been required to construct model selection criteria, relaxing the assumptions imposed AIC and BIC
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
About this book
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Reviews
From the Reviews:
"I was fully satisfied with it. The authors are obviously well-qualified to write on the subject." (Biometrics Book Reviews, 2008)
"This book explains the basic ideas of model evaluation and presents the definition and derivation of the AIC and related criteria, including BIC. … The book makes a major contribution to the understanding of statistical modeling. Researchers interested in statistical modeling will find a lot of interesting material in it."(Erkki P. Liski, International Statistical Reviews, Vol. 76 (2), 2008)
“…Modeling is an important and challenging endeavor that permeates nearly all aspects of applied statistics. The validity of inferences, predictions, and conclusions depends on the propriety of the model serving as their basis. Any book that improves the ability of practicing statisticians and biostatisticians to formulate, select and use models is worth its weight in gold. Konishi and Kitagawa have written such a book.” (Journal of the American Statistical Association September 2009, Vol. 104, No. 487, Book Reviews)
“With the main purpose of explaining the critical role of information criteria in statistical modeling, this book is written by two leading experts. … The book ends with a list of references and an index. The style of writing is very good. Examples illustrate the concepts discussed and make the book immensely readable. … Anybody interested in statistical modeling will love to read this book. … it will be very useful to researchers and students interested in learning statistical modeling and model evaluation.” (Ravi Sreenivasan, Zentralblatt MATH, Vol. 1172, 2009)
Authors and Affiliations
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Kyushu University, 812-8581, Japan
Sadanori Konishi
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106-8569, Minato-ku, Japan
Genshiro Kitagawa
Bibliographic Information
Book Title: Information Criteria and Statistical Modeling
Authors: Sadanori Konishi, Genshiro Kitagawa
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-0-387-71887-3
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag New York 2008
Hardcover ISBN: 978-0-387-71886-6Published: 12 October 2007
Softcover ISBN: 978-1-4419-2456-8Published: 23 November 2010
eBook ISBN: 978-0-387-71887-3Published: 12 September 2007
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XII, 276
Topics: Statistical Theory and Methods, Mathematical Modeling and Industrial Mathematics, Coding and Information Theory, Data Mining and Knowledge Discovery, Probability and Statistics in Computer Science, Simulation and Modeling